(a) Field of the Invention
The present invention generally pertains to statistical classifiers and, more specifically, to a method for improving the performance of statistical classifiers.
(b) Description of Related Art
Statistical classifiers attempt to classify objects or events into categories based on information provided from information sources observing the object or event. Historically, statistical classifiers have been used in applications such as financial engineering and expert systems. Other applications of statistical classifiers include target recognition situations.
Traditionally, Bayesian probability calculus, which is well known, has been used to construct statistical classifiers. Classical Bayesian techniques allow inferences to be made based on information from information sources. Bayesian techniques work well when representative statistics (i.e., data representing a real situation) are available to be used when training the classifier. In many real-world situations there are either no representative statistics available to be used for training, or the statistics available are unreliable. This eliminates the opportunity to train the classifier adequately for a particular application. Also, insufficient or inaccurate prior statistics can cause conflict in the statistical classifier. If the statistical classifier relies on Bayesian probability calculus, the conflict may cause the classifier to become unstable.
Often it is advantageous to combine independent sources of information to enable the classifier to make more inferences as to the identity of the object or event being observed. However, when multiple information sources are used (as is typically the case) there may be conflict between the information sources. As previously noted, conflicting information may seriously de-stabilize a statistical classifier that employs the Bayesian technique, leading to inaccurate conclusions by the classifier.
Consider a target recognition scenario wherein there are two target types (t1 and t2) and two decoy types (d1 and d2). Three sensors (sensor 1, sensor 2, and sensor 3) may be used to provide information in an effort to determine the identity of an object which is observed. Sensor 1 may report .mu.1(t1)=1, meaning that sensor 1 has 100% confidence that the object under observation is t1. However, it may be the case that sensor 2 reports .mu.2(d2)=1, meaning that sensor 2 has 100% confidence that the object being observed is d2. Furthermore, it may be the case that sensor 3 reports .mu.3(t1)=1. Two of the three sources agree while a third is in conflict. Conflicting sources of information will confuse the statistical classifier because the classifier has no idea which piece of information is accurate.
Therefore, there is a need for a statistical classifier that can resolve conflict in independent sources of information, thus creating a robust classifier that has superior performance to classifiers currently available. Additionally, there is a need for a statistical classifier which does not need entirely reliable prior statistics.